R.L. Anderson

Bio: R.L. Anderson is an academic researcher. The author has contributed to research in topics: Loop (topology) & Detector. The author has an hindex of 1, co-authored 1 publications receiving 49 citations.

Electromagnetic loop vehicle detectors

Abstract: Three different electromagnetic loop vehicle detector designs are described: self-tuning, bridge balance, and phase-shift. Principles of operation, design limitations, and design trade-offs for each method are presented in detail. The characteristics of the lead-in wire used are shown to be the primary limitation in loop detector performance and stability. Characteristics of commercially available wire used in present-day loop detector installations are discussed. Design equations and graphs illustrate the tradeoff considerations in the determination of optimum loop configuration and inductance.

Smart Parking Guidance, Monitoring and Reservations: A Review

Abstract: As the urban population is increasing, more and more cars are circulating in the city to search for parking spaces which contributes to the global problem of traffic congestion. To alleviate the parking problems, smart parking systems must be implemented. In this paper, the background on parking problems is introduced and relevant algorithms, systems, and techniques behind the smart parking are reviewed and discussed. This paper provides a good insight into the guidance, monitoring and reservations components of the smart car parking and directions to the future development.

A Multiple Inductive Loop Vehicle Detection System for Heterogeneous and Lane-Less Traffic

Abstract: This paper presents a novel inductive loop sensor that can detect vehicles under a heterogeneous and less-lane-disciplined traffic and thus can be used to support a traffic control management system in optimizing the best use of existing roads. The loop sensor proposed in this paper detects large (e.g., bus) as well as small (e.g., bicycle) vehicles occupying any available space in the roadway, which is the main requirement for sensing heterogeneous and lane-less traffic. To accomplish the sensing of large as well as small vehicles, a multiple loop system with a new inductive loop sensor structure is proposed. The proposed sensor structure not only senses and segregates the vehicle type as bicycle, motor cycle, scooter, car, and bus but also enables accurate counting of the number of vehicles even in a mixed traffic flow condition. A prototype of the multiple loop sensing system has been developed and tested. Field tests indicate that the prototype successfully detected all types of vehicles and counted, correctly, the number of each type of vehicles. Thus, the suitability of the proposed sensor system for any type of traffic has been established.

Parking monitor system based on magnetic field senso

Abstract: To easily find an unoccupied parking space in a large car park is a problem for many drivers. Thus it is useful to have technical solutions which can provide information on parking space occupancy. A new monitor system is described in the following. It is based on passive magnetic field sensors. It provides occupancy information for car park users and helps them to place the car in a most efficient way

Sensing and Signal Processing for Vehicle Reidentification and Travel Time Estimation

Abstract: Link travel times are crucial for advanced traveler information systems and traffic management applications. However, current systems for estimating them still have shortcomings that need to be addressed. In this paper, we propose a novel framework for vehicle reidentification via signature matching using signal processing techniques and a travel time estimation algorithm that is robust to potential (and often inevitable) vehicle misidentifications. Individual vehicles are matched between well-separated stations in a road transportation network using signatures captured by embedded roadway sensors. Statistical and multirate signal processing methods are used to develop data-postprocessing algorithms that are critical to the subsequent signature-matching problem, which is formulated using optimal techniques from communication theory. A probabilistic modeling of the generated matching assignments and an unsupervised data-clustering technique are then used to devise a travel time estimation algorithm. The proposed method is tested under a real traffic scenario, and accurate link travel time measures are reported.

Evaluation of a neighbor-based vehicle density estimation scheme

Abstract: Vehicle density is one of the main metrics used for assessing the road traffic condition. A high vehicle density usually indicates that the traffic is congested. Currently, most of the vehicle density estimation approaches are designed for the infrastructure-based traffic information systems. These approaches require detecting devices such as inductive loop detectors or traffic surveillance cameras to be installed at various locations. Consequently, they are not suitable for an emerging self-organizing vehicular traffic information system, where the vehicles have to collect and process the traffic information without relying on any fixed infrastructure. In this paper, we consider a method for estimating the vehicle density based on the number of vehicles in the vicinity of the probe vehicle and evaluate its performance.